Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583677
Wenxiu Zhang, Y. Leung
Uncertainty inference is a key problem in artificial intelligence. The concept of including degree is introduced and some methods of generating including degrees are given. Some applications of including degrees to the retrieval and inference in expert systems are demonstrated.
{"title":"Theory of including degrees and its applications to uncertainty inferences","authors":"Wenxiu Zhang, Y. Leung","doi":"10.1109/AFSS.1996.583677","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583677","url":null,"abstract":"Uncertainty inference is a key problem in artificial intelligence. The concept of including degree is introduced and some methods of generating including degrees are given. Some applications of including degrees to the retrieval and inference in expert systems are demonstrated.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114228150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583649
Weimin Pan, Li Shen
This paper generalizes operator fuzzy logic to operator fuzzy modal logic. Some properties, such as, reduction and transformation of modalities, are discussed. Based on the definitions and properties depicted above, clauses set and standard clauses set are proposed. It is proved that for every clauses set there is a standard clauses set which is logically equal to it. This paper defines /spl lambda/-unsatisfiable and /spl lambda/-reduced set of standard clauses set, and proves that standard clauses set is /spl lambda/-unsatisfiable if and only if its /spl lambda/-reduced set is unsatisfiable. Therefore, any clauses set is /spl lambda/-unsatisfiable if and only if the /spl lambda/-reduced set of its standard clauses set is unsatisfiable. Based on the author's previous works, /spl lambda/-reduced set is unsatisfiable if and only if the empty clause can be derived from its resolvable form. Then we complete /spl lambda/-resolution on an operator fuzzy modal logic system.
{"title":"Operator fuzzy modal logic and principle of resolution","authors":"Weimin Pan, Li Shen","doi":"10.1109/AFSS.1996.583649","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583649","url":null,"abstract":"This paper generalizes operator fuzzy logic to operator fuzzy modal logic. Some properties, such as, reduction and transformation of modalities, are discussed. Based on the definitions and properties depicted above, clauses set and standard clauses set are proposed. It is proved that for every clauses set there is a standard clauses set which is logically equal to it. This paper defines /spl lambda/-unsatisfiable and /spl lambda/-reduced set of standard clauses set, and proves that standard clauses set is /spl lambda/-unsatisfiable if and only if its /spl lambda/-reduced set is unsatisfiable. Therefore, any clauses set is /spl lambda/-unsatisfiable if and only if the /spl lambda/-reduced set of its standard clauses set is unsatisfiable. Based on the author's previous works, /spl lambda/-reduced set is unsatisfiable if and only if the empty clause can be derived from its resolvable form. Then we complete /spl lambda/-resolution on an operator fuzzy modal logic system.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115320072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583625
R. Chen, T.Y. Lin
Earlier a "new" rough set theory for very large databases was proposed by T.Y. Lin (1996). In this paper the authors attempt to evaluate the performance of such a rough set theory for a very large database. ORACLE, a relational database management system (RDBMS), is the market leader in open system databases. Windows NT has been growing into the data server environment and is a strong contender for decision support system applications with its open and cost-effective architecture. So ORACLE running under Windows NT was used in this report. The main goal of this research is to formulate a suitable rough set theory for very large databases.
{"title":"Supporting rough set theory in very large databases using oracle RDBMS","authors":"R. Chen, T.Y. Lin","doi":"10.1109/AFSS.1996.583625","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583625","url":null,"abstract":"Earlier a \"new\" rough set theory for very large databases was proposed by T.Y. Lin (1996). In this paper the authors attempt to evaluate the performance of such a rough set theory for a very large database. ORACLE, a relational database management system (RDBMS), is the market leader in open system databases. Windows NT has been growing into the data server environment and is a strong contender for decision support system applications with its open and cost-effective architecture. So ORACLE running under Windows NT was used in this report. The main goal of this research is to formulate a suitable rough set theory for very large databases.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"249 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114057887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583707
N. Yubazaki, J. Yi, M. Otani, K. Hirota
SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.
{"title":"SIRM's connected fuzzy inference model and its applications to first-order lag systems and second-order lag systems","authors":"N. Yubazaki, J. Yi, M. Otani, K. Hirota","doi":"10.1109/AFSS.1996.583707","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583707","url":null,"abstract":"SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133297582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583602
I. Chiang, Jane Yung-jen Hsu
It is often difficult to make accurate predictions, given uncertain and noisy data for classification. Unfortunately, most real-world problems have to deal with such imperfect data. This paper presents a new model for fuzzy classification by integrating fuzzy classifiers with decision trees. In this approach, a fuzzy classification tree is constructed from the training data set. Instead of defining a specific class for a given instance, the proposed fuzzy classification scheme computes its degree of possibility for each class. The performance of the system is evaluated by empirically compared with a standard decision tree classifier C4.5 on several benchmark data sets from the UCI machine learning repository.
{"title":"Integration of fuzzy classifiers with decision trees","authors":"I. Chiang, Jane Yung-jen Hsu","doi":"10.1109/AFSS.1996.583602","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583602","url":null,"abstract":"It is often difficult to make accurate predictions, given uncertain and noisy data for classification. Unfortunately, most real-world problems have to deal with such imperfect data. This paper presents a new model for fuzzy classification by integrating fuzzy classifiers with decision trees. In this approach, a fuzzy classification tree is constructed from the training data set. Instead of defining a specific class for a given instance, the proposed fuzzy classification scheme computes its degree of possibility for each class. The performance of the system is evaluated by empirically compared with a standard decision tree classifier C4.5 on several benchmark data sets from the UCI machine learning repository.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134098947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583709
Ying Li, Yua-Tong Jang
The paper presents two complex adaptive fuzzy inference systems, CANFIS and CFBFN. CANFIS is a complex extension of ANFIS. CFBFN is a variation of CANFIS where the number of fuzzy rules can be chosen with greater flexibility. These systems can be used in such applications as communications, radar, and sonar, where the information bearing signals are usually complex.
{"title":"Complex adaptive fuzzy inference systems","authors":"Ying Li, Yua-Tong Jang","doi":"10.1109/AFSS.1996.583709","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583709","url":null,"abstract":"The paper presents two complex adaptive fuzzy inference systems, CANFIS and CFBFN. CANFIS is a complex extension of ANFIS. CFBFN is a variation of CANFIS where the number of fuzzy rules can be chosen with greater flexibility. These systems can be used in such applications as communications, radar, and sonar, where the information bearing signals are usually complex.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123017888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583683
T. Chu, Chung-Tsen Tsao, Yeou-Ren Shiue
The investor has to consider many factors when making a decision on which stocks to buy. However, judgements on these factors are usually linguistic, fuzzy, and conflicting. Therefore, selection of stocks is a fuzzy multiple attribute decision making (FMADM) problems. A hierarchical composite structure for factors and subfactors is developed for company analysis. A weight model is presented. Values of each subfactor are assumed to have normal distribution in order to build up the membership function of the ascending half-trapezoid. By multiplying the weight matrix with the corresponding fuzzy judgement matrix for each factor and calculating the weighted summation of weighted matrices, the authors make the fuzzy decision by grades. A numerical example of selecting the first priority stock among seven listed companies of the cement industry in Taiwan's stock market is applied to verify this model.
{"title":"Application of fuzzy multiple attribute decision making on company analysis for stock selection","authors":"T. Chu, Chung-Tsen Tsao, Yeou-Ren Shiue","doi":"10.1109/AFSS.1996.583683","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583683","url":null,"abstract":"The investor has to consider many factors when making a decision on which stocks to buy. However, judgements on these factors are usually linguistic, fuzzy, and conflicting. Therefore, selection of stocks is a fuzzy multiple attribute decision making (FMADM) problems. A hierarchical composite structure for factors and subfactors is developed for company analysis. A weight model is presented. Values of each subfactor are assumed to have normal distribution in order to build up the membership function of the ascending half-trapezoid. By multiplying the weight matrix with the corresponding fuzzy judgement matrix for each factor and calculating the weighted summation of weighted matrices, the authors make the fuzzy decision by grades. A numerical example of selecting the first priority stock among seven listed companies of the cement industry in Taiwan's stock market is applied to verify this model.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583576
T. Murai, M. Nakata, M. Shimbo
A modal logical explanation is presented about how data conveyed by logical formulas generates relations, or tables, in databases as belief sets based on the idea of possible worlds restriction.
{"title":"Generation of relations as belief in databases","authors":"T. Murai, M. Nakata, M. Shimbo","doi":"10.1109/AFSS.1996.583576","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583576","url":null,"abstract":"A modal logical explanation is presented about how data conveyed by logical formulas generates relations, or tables, in databases as belief sets based on the idea of possible worlds restriction.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583542
A. Wasilewska
A concept of LT-fuzzy sets was introduced by Rasiowa and Cat Ho (1992). LT-fuzzy sets are a modification of L-fuzzy sets introduced by Goguen (1967). We introduce here a notion of a generalized rough set and show that it can be considered as a particular case of a L-fuzzy set. We also generalize the notion of a rough equality of sets, introduced by Pawlak in 1985 to a notion of topological equality of sets and we prove that the LT-fuzzy sets provide a common characterization for all of the considered concepts.
{"title":"On rough and LT-fuzzy sets","authors":"A. Wasilewska","doi":"10.1109/AFSS.1996.583542","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583542","url":null,"abstract":"A concept of LT-fuzzy sets was introduced by Rasiowa and Cat Ho (1992). LT-fuzzy sets are a modification of L-fuzzy sets introduced by Goguen (1967). We introduce here a notion of a generalized rough set and show that it can be considered as a particular case of a L-fuzzy set. We also generalize the notion of a rough equality of sets, introduced by Pawlak in 1985 to a notion of topological equality of sets and we prove that the LT-fuzzy sets provide a common characterization for all of the considered concepts.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116846080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583593
Ching-Hsue Cheng, Yinghai Liu, Yin-Fang Lin
We propose a new algorithm for evaluating a guided missile destroyer by catastrophe series based on fuzzy scales, which is a multiple criteria decision making approach in a fuzzy environment. The descriptions and judgements on weapon systems are usually linguistic and fuzzy. We use the triangular fuzzy number 1/spl tilde/, 3/spl tilde/, 5/spl tilde/, 7/spl tilde/, and 9/spl tilde/ to indicate the relative strength of the elements in the judgement matrix, its elements use a comparison of the performance scores. Our method does not require the subjective weight vector with the corresponding criteria. It utilizes catastrophe series to evaluate multi-goal decision making problems, including cusp, swallowtail and butterfly catastrophe models. However, it is subject to criteria /spl les/4 for decision making problems. Therefore, we derive an extension formula for decision making problems with 5 criteria, which is a wigwam catastrophe model. The catastrophe series method is used in a multiple attribute decision making problem in a conflict environment (i.e., all criteria have different degrees of importance). A fuzzy decision problem is solved and an example of weapon system selection is used to illustrate our method and compare it with other methods.
{"title":"Evaluating a weapon system using catastrophe series based on fuzzy scales","authors":"Ching-Hsue Cheng, Yinghai Liu, Yin-Fang Lin","doi":"10.1109/AFSS.1996.583593","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583593","url":null,"abstract":"We propose a new algorithm for evaluating a guided missile destroyer by catastrophe series based on fuzzy scales, which is a multiple criteria decision making approach in a fuzzy environment. The descriptions and judgements on weapon systems are usually linguistic and fuzzy. We use the triangular fuzzy number 1/spl tilde/, 3/spl tilde/, 5/spl tilde/, 7/spl tilde/, and 9/spl tilde/ to indicate the relative strength of the elements in the judgement matrix, its elements use a comparison of the performance scores. Our method does not require the subjective weight vector with the corresponding criteria. It utilizes catastrophe series to evaluate multi-goal decision making problems, including cusp, swallowtail and butterfly catastrophe models. However, it is subject to criteria /spl les/4 for decision making problems. Therefore, we derive an extension formula for decision making problems with 5 criteria, which is a wigwam catastrophe model. The catastrophe series method is used in a multiple attribute decision making problem in a conflict environment (i.e., all criteria have different degrees of importance). A fuzzy decision problem is solved and an example of weapon system selection is used to illustrate our method and compare it with other methods.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"544 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123228757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：